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arXiv 2025年

作者： Saifullah, Shoffan Dreżewski, Rafal Faculty of Computer Science AGH University of Krakow Krakow Poland Department of Informatics Universitas Pembangunan Nasional Veteran Yogyakarta Yogyakarta Indonesia

Medical image segmentation, particularly for brain tumor analysis, demands precise and computationally efficient models due to the complexity of multimodal MRI datasets and diverse tumor morphologies. This study introduces PSO-UNet, which integrates Particle Swarm Optimization (PSO) with the U-Net architecture for dynamic hyperparameter optimization. Unlike traditional manual tuning or alternative optimization approaches, PSO effectively navigates complex hyperparameter search spaces, explicitly optimizing the number of filters, kernel size, and learning rate. PSO-UNet substantially enhances segmentation performance, achieving Dice Similarity Coefficients (DSC) of 0.9578 and 0.9523 and Intersection over Union (IoU) scores of 0.9194 and 0.9097 on the BraTS 2021 and Figshare datasets, respectively. Moreover, the method reduces computational complexity significantly, utilizing only 7.8 million parameters and executing in approximately 906 seconds, markedly faster than comparable U-Net-based frameworks. These outcomes underscore PSO-UNet’s robust generalization capabilities across diverse MRI modalities and tumor classifications, emphasizing its clinical potential and clear advantages over conventional hyperparameter tuning methods. Future research will explore hybrid optimization strategies and validate the framework against other bio-inspired algorithms to enhance its robustness and *** Codes 68Q07 © 2025, CC BY.

关键词： Particle swarm optimization (PSO)

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Sustainable energy management in the AI era: a comprehensive analysis of ML and DL approaches

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Computing 2025年第6期107卷 1-64页

作者： Javed, Haseeb Eid, Fatma El-Sappagh, Shaker Abuhmed, Tamer Department of Computer Science and Engineering College of Computing and Informatics Sungkyunkwan University Suwon South Korea Technology Management Stony Brook University New York USA Applied Artificial Intelligence College of Computing and Informatics Sungkyunkwan University Suwon South Korea Faculty of Computer Science and Engineering Galala University Suez Egypt Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Banha Egypt

This study comprehensively analyzes the application of innovative deep learning (DL) and machine learning (ML) techniques in smart energy management systems (EMSs), with an emphasis on load forecasting, demand response, and the development of smart energy sectors. The application of various ML and DL models were examined in over 200 studies from 2014 to 2024 in an electrical network's EMS to highlight the key benefits and advances made by each technology for the sustainable management systems in energy sector. The findings emphasize DL and ML models’ enhanced precision and predictive capabilities in load forecasting, their efficacy in enabling efficient demand response mechanisms, and their significance in supporting the development of smart energy sectors. Furthermore, recommendations are made based on the survey results to assist in incorporating these techniques into EMS frameworks, such as investment in data infrastructure, model training and validation, and collaboration between researchers, industry experts, and policymakers. The study also discusses the limitations identified in the literature, such as limited real-world implementations, challenges regarding quality and data availability, and the need for enhanced ML and DL model interpretability. Addressing these limitations can assist in increasing the application and efficacy of ML and DL techniques in EMSs, enabling a more efficient and sustainable energy landscape. Finally, this study facilitates researchers' exploration of ML and DL in energy management, highlighting relevant limitations, strengths, and alternative approaches associated with sustainable energy management. It also indicates potential future research directions for further investigation.

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A systematic review on cybersecurity of robotic systems: vulnerabilities trends, threats, attacks, challenges, and proposed framework

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International Journal of Information Security 2025年第3期24卷 1-37页

作者： Verma, Narinder Kumar, Neerendra Verma, Chaman Illés, Zoltán Singh, Deep Department of Computer Science and IT Central University of Jammu Jammu181143 India Department of Media and Educational Informatics Faculty of Informatics Eötvös Loránd University Budapest1053 Hungary Department of Mathematics and Statistics Central University of Punjab Bathinda151401 India

Robotic systems are increasingly used in diverse applications such as healthcare, manufacturing, and critical infrastructure to automate day-to-day tasks. In this study, the existing state-of-the-art robotic system cybersecurity mechanisms are reviewed. Consequently, key aspects of the security mechanisms are highlighted. The study involves an analysis of applications in different domains, communication protocols, architecture, threats, and attacks associated with robotic systems. The systematic review of the existing work comprises inclusion and exclusion criteria, research protocol, initial search and keywords, searching, methodology diagram, research questions, and paper selection process. After analyzing the National Vulnerabilities Database, existing studies, and online resources, the resultant vulnerability trends are presented. The research asserts that cybersecurity measures including secure design, development, access control, and authentication are essential for robotic systems. Further, different machine learning, encryption, and blockchain-based mechanisms may be used to prevent or detect cyber-attacks in robotic systems. Based on comprehensive analysis, a cybersecurity framework is proposed to address the challenges effectively. The framework can be utilized to mitigate cybersecurity risks in robotic systems. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Cyber attacks

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Practical big data techniques for end-to-end machine learning deployment: a comprehensive review

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Discover Data 2025年第1期3卷 1-18页

作者： El-Sayed, Ahmed Abougabal, Mohamed Lazem, Shaimaa Computer and Systems Engineering Department Faculty of Engineering Alexandria University Alexandria Egypt Informatics Research Institute City of Scientific Research and Technological Applications New Borg El-Arab Egypt

In recent years, data generated from diverse sources has grown exponentially, giving rise to new challenges for processing and analysis under the umbrella of “big data". Although various methods and platforms have been proposed to tackle these issues, there is still a need for a conceptual guide that synthesizes state-of-the-art tools and techniques across the full machine learning (ML) workflow. In this survey, we consolidate research on big data infrastructures, distributed processing frameworks, and ML methods, mapping out an end-to-end conceptual framework that can serve as a reference architecture for end-to-end deployment. Specifically, we discuss key aspects of big data analytics (storage solutions, platforms like Hadoop and Spark, NoSQL databases), preprocessing approaches (dimensionality reduction, instance selection, noise handling), ML algorithms (supervised, unsupervised, and emerging deep learning paradigms), and deployment considerations (monitoring, continuous integration, and versioning). By gathering and integrating these elements, our survey provides a comprehensive overview of existing solutions, clarifies the design choices developers must consider, and identifies research gaps that remain to be addressed.

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Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (Science and Information Organization). All rights reserved.

关键词： Quantum cryptography

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Enhancing Solar Photovoltaic Efficiency:A Computational Fluid Dynamics Analysis

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Energy Engineering 2025年第1期122卷 153-166页

作者： Rahool Rai Fareed Hussain Mangi Kashif Ahmed Sudhakar Kumaramsay Faculty of Mechanical and Automotive Engineering Universiti Malaysia Pahang Al-Sultan AbdullahPekan26600PahangMalaysia Faculty of Engineering Science&Technology Hamdard UniversityKarachi74600Pakistan Electrical Engineering Department SUKKUR IBA UniversitySukkur65200Pakistan Berlin School of Business and Innovation BSBI Faculty of Computer Science and InformaticsBerlin12043Germany Automotive Engineering Centre University Malaysia Pahang Al Sultan AbdullahPekan26600PahangMalaysia Centre for Research in Advanced Fluid&Processes(Fluid Centre) Universiti Malaysia Pahang Al-Sultan Abdullah(UMPSA)Paya BesarKuantan26300PahangMalaysia

The growing need for sustainable energy solutions,driven by rising energy shortages,environmental concerns,and the depletion of conventional energy sources,has led to a significant focus on renewable *** energy,among the various renewable sources,is particularly appealing due to its abundant ***,the efficiency of commercial solar photovoltaic(PV)modules is hindered by several factors,notably their conversion efficiency,which averages around 19%.This efficiency can further decline to 10%–16%due to temperature increases during peak sunlight *** study investigates the cooling of PV modules by applying water to their front surface through Computational fluid dynamics(CFD).The study aimed to determine the optimal conditions for cooling the PV module by analyzing the interplay between water film thickness,Reynolds number,and their effects on temperature reduction and heat *** CFD analysis revealed that the most effective cooling condition occurred with a 5 mm thick water film and a Reynolds number of *** specific parameters were found to maximize the heat transfer and temperature reduction *** finding is crucial for the development of practical and efficient cooling systems for PV modules,potentially leading to improved performance and longevity of solar *** cooling fluids or advanced cooling techniques that might offer even better efficiency or practical benefits.

关键词： PV module efficiency water film thickness reynolds number CFD analysis PV/T renewable energy

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A STUDY ON SOME CLASSES OF PANTOGRAPH LANGEVIN FRACTIONAL DIFFERENTIAL PROBLEMS WITH NONLOCAL CONDITIONS

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Journal of Mathematical Sciences (United States) 2025年 1-18页

作者： Bouzid, Houari Benali, Abdelkader Salim, Abdelkrim Erhan, İnci M. Department of Mathematics Faculty of Exact Science and Informatics Laboratory of Mathematics and Applications (LMA) Hassiba Benbouali University of Chlef Ouled Fares Chlef 02000 Algeria Faculty of Technology Hassiba Benbouali University of Chlef Chlef 02000 Algeria Laboratory of Mathematics Djillali Liabes University of Sidi Bel-Abbes Sidi Bel-Abbes 22000 Algeria Department of Computer Engineering Aydın Adnan Menderes University Aydın 09010 Turkey

This paper concentrates on the examination of a class of nonlocal initial value problems for Langevin fractional differential equations featuring the ψ-Caputo fractional derivative within Banach spaces. By utilizing Banach’s fixed-point theorem, we establish the uniqueness of the solutions to these problems. The existence of solutions is then confirmed using Schaefer’s and Darbo’s fixed-point theorems. Finally, we provide examples to illustrate our findings. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Darbo theorem Fractional differential equations (FDE) Measure of noncompactness ψ-fractional integral

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Attackers Can Do Better: Over- and Understated Factors of Model Stealing Attacks

arXiv

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arXiv 2025年

作者： Oliynyk, Daryna Mayer, Rudolf Rauber, Andreas CDL AsTra Faculty of Computer Science University of Vienna Vienna Austria SBA Research Vienna Austria Institute of Information Systems Engineering Faculty of Informatics TU Wien Vienna Austria

Machine learning (ML) models were shown to be vulnerable to model stealing attacks, which lead to intellectual property infringement. Among other attack methods, substitute model training is an all-encompassing attack applicable to any machine learning model whose behaviour can be approximated from input-output queries. Whereas previous works mainly focused on improving the performance of substitute models by, e.g. developing a new substitute training method, there have been only limited ablation studies that try to understand the impact the strength of an attacker has on the substitute model’s performance. As a result, different authors came to diverse, sometimes contradicting, conclusions. In this work, we exhaustively examine the ambivalent influence of different factors resulting from varying the attacker’s capabilities and knowledge on a substitute training attack. Our findings suggest that some of the factors that have been considered important in the past are, in fact, not that influential;instead, we discover new correlations between attack conditions and success rate. In particular, we demonstrate that better-performing target models enable higher-fidelity attacks and explain the intuition behind this phenomenon. Further, we propose to shift the focus from the complexity of target models toward the complexity of their learning tasks. Therefore, for the substitute model, rather than aiming for a higher architecture complexity, we suggest focusing on getting data of higher complexity and an appropriate architecture. Finally, we demonstrate that even in the most limited data-free scenario, there is no need to overcompensate weak knowledge with unrealistic capabilities in the form of millions of queries. Our results often exceed or match the performance of previous attacks that assume a stronger attacker, suggesting that these stronger attacks are likely endangering a model owner’s intellectual property to a significantly higher degree than shown until now. Cop

关键词： Adversarial machine learning

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Attackers Can Do Better: Over- and Understated Factors of Model Stealing Attacks

Attackers Can Do Better: Over- and Understated Factors of Mo...

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Secure and Trustworthy Machine Learning (SaTML), IEEE Conference on

作者： Daryna Oliynyk Rudolf Mayer Andreas Rauber CDL AsTra Faculty of Computer Science University of Vienna Vienna Austria SBA Research Vienna Austria Faculty of Informatics TU Wien Institute of Information Systems Engineering Vienna Austria

ISBN: (数字)9798331517113

ISBN: (纸本)9798331517120

Machine learning (ML) models were shown to be vulnerable to model stealing attacks, which lead to intellectual property infringement. Among other attack methods, substitute model training is an all-encompassing attack applicable to any machine learning model whose behaviour can be approximated from input-output queries. Whereas previous works mainly focused on improving the performance of substitute models by, e.g. developing a new substitute training method, there have been only limited ablation studies that try to understand the impact the strength of an attacker has on the substitute model's performance. As a result, different authors came to diverse, sometimes contradicting, conclusions. In this work, we exhaustively examine the ambivalent influence of different factors resulting from varying the attacker's capabilities and knowledge on a substitute training attack. Our findings suggest that some of the factors that have been considered important in the past are, in fact, not that influential; instead, we discover new correlations between attack conditions and success rate. In particular, we demonstrate that better-performing target models enable higher-fidelity attacks and explain the intuition behind this phenomenon. Further, we propose to shift the focus from the complexity of target models toward the complexity of their learning tasks. Therefore, for the substitute model, rather than aiming for a higher architecture complexity, we suggest focusing on getting data of higher complexity and an appropriate architecture. Finally, we demonstrate that even in the most limited data-free scenario, there is no need to overcompensate weak knowledge with unrealistic capabilities in the form of millions of queries. Our results often exceed or match the performance of previous attacks that assume a stronger attacker, suggesting that these stronger attacks are likely endangering a model owner's intellectual property to a significantly higher degree than shown until now.

关键词： Training Correlation Query processing Focusing Intellectual property Data collection Data models Adversarial machine learning Complexity theory Tuning

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SEMU: Singular Value Decomposition for Efficient Machine Unlearning

arXiv

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arXiv 2025年

作者： Sendera, Marcin Struski, Lukasz Ksiazek, Kamil Musiol, Kryspin Tabor, Jacek Rymarczyk, Dawid Faculty of Mathematics and Computer Science Jagiellonian University Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Poland Ardigen SA Poland

While the capabilities of generative foundational models have advanced rapidly in recent years, methods to prevent harmful and unsafe behaviors remain underdeveloped. Among the pressing challenges in AI safety, machine unlearning (MU) has become increasingly critical to meet upcoming safety regulations. Most existing MU approaches focus on altering the most significant parameters of the model. However, these methods often require fine-tuning substantial portions of the model, resulting in high computational costs and training instabilities, which are typically mitigated by access to the original training dataset. In this work, we address these limitations by leveraging Singular Value Decomposition (SVD) to create a compact, low-dimensional projection that enables the selective forgetting of specific data points. We propose Singular Value Decomposition for Efficient Machine Unlearning (SEMU), a novel approach designed to optimize MU in two key aspects. First, SEMU minimizes the number of model parameters that need to be modified, effectively removing unwanted knowledge while making only minimal changes to the model’s weights. Second, SEMU eliminates the dependency on the original training dataset, preserving the model’s previously acquired knowledge without additional data requirements. © 2025, CC BY.

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